Overview

Dataset statistics

Number of variables43
Number of observations10836
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 MiB
Average record size in memory344.0 B

Variable types

Categorical28
Numeric14
DateTime1

Warnings

lifecycle:transition has constant value "complete" Constant
case has a high cardinality: 776 distinct values High cardinality
hour is highly correlated with timesincemidnightHigh correlation
timesincemidnight is highly correlated with hourHigh correlation
timesincelast is highly correlated with timesincestartHigh correlation
timesincestart is highly correlated with timesincelastHigh correlation
SIRSCritHeartRate is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
DiagnosticSputum is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
DiagnosticBlood is highly correlated with DiagnosticLiquor and 4 other fieldsHigh correlation
InfectionSuspected is highly correlated with SIRSCriteria2OrMore and 4 other fieldsHigh correlation
SIRSCriteria2OrMore is highly correlated with InfectionSuspected and 4 other fieldsHigh correlation
DiagnosticLiquor is highly correlated with SIRSCritHeartRate and 22 other fieldsHigh correlation
concept:name is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
SIRSCritTemperature is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
Infusion is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
Oligurie is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
Hypoxie is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
DiagnosticArtAstrup is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
DiagnosticUrinarySediment is highly correlated with DiagnosticLiquor and 3 other fieldsHigh correlation
Hypotensie is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
lifecycle:transition is highly correlated with SIRSCritHeartRate and 25 other fieldsHigh correlation
Diagnose is highly correlated with lifecycle:transitionHigh correlation
DiagnosticUrinaryCulture is highly correlated with DiagnosticLiquor and 3 other fieldsHigh correlation
SIRSCritLeucos is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
org:group is highly correlated with lifecycle:transitionHigh correlation
DiagnosticIC is highly correlated with DiagnosticBlood and 6 other fieldsHigh correlation
label is highly correlated with lifecycle:transitionHigh correlation
DiagnosticOther is highly correlated with SIRSCritHeartRate and 22 other fieldsHigh correlation
DiagnosticECG is highly correlated with DiagnosticLiquor and 3 other fieldsHigh correlation
DisfuncOrg is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
DiagnosticLacticAcid is highly correlated with DiagnosticBlood and 4 other fieldsHigh correlation
DiagnosticXthorax is highly correlated with DiagnosticLiquor and 3 other fieldsHigh correlation
SIRSCritTachypnea is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
Leucocytes has 3069 (28.3%) zeros Zeros
CRP has 3408 (31.5%) zeros Zeros
LacticAcid has 4027 (37.2%) zeros Zeros
weekday has 1639 (15.1%) zeros Zeros
hour has 175 (1.6%) zeros Zeros
timesincelast has 3596 (33.2%) zeros Zeros
timesincestart has 794 (7.3%) zeros Zeros
remainingtime has 677 (6.2%) zeros Zeros

Reproduction

Analysis started2021-03-23 07:51:38.059970
Analysis finished2021-03-23 07:52:10.117096
Duration32.06 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

InfectionSuspected
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
other
10060 
True
 
688
False
 
88

Length

Max length5
Median length5
Mean length4.936507937
Min length4

Characters and Unicode

Total characters53492
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other10060
92.8%
True688
 
6.3%
False88
 
0.8%
2021-03-23T08:52:10.244964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:52:10.300830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other10060
92.8%
true688
 
6.3%
false88
 
0.8%

Most occurring characters

ValueCountFrequency (%)
e10836
20.3%
r10748
20.1%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T688
 
1.3%
u688
 
1.3%
F88
 
0.2%
a88
 
0.2%
l88
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter52716
98.5%
Uppercase Letter776
 
1.5%

Most frequent character per category

ValueCountFrequency (%)
e10836
20.6%
r10748
20.4%
o10060
19.1%
t10060
19.1%
h10060
19.1%
u688
 
1.3%
a88
 
0.2%
l88
 
0.2%
s88
 
0.2%
ValueCountFrequency (%)
T688
88.7%
F88
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
Latin53492
100.0%

Most frequent character per script

ValueCountFrequency (%)
e10836
20.3%
r10748
20.1%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T688
 
1.3%
u688
 
1.3%
F88
 
0.2%
a88
 
0.2%
l88
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII53492
100.0%

Most frequent character per block

ValueCountFrequency (%)
e10836
20.3%
r10748
20.1%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T688
 
1.3%
u688
 
1.3%
F88
 
0.2%
a88
 
0.2%
l88
 
0.2%

org:group
Categorical

HIGH CORRELATION

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
B
5268 
A
2714 
C
777 
E
671 
?
 
247
Other values (20)
1159 

Length

Max length5
Median length1
Mean length1.00073828
Min length1

Characters and Unicode

Total characters10844
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowA
2nd rowB
3rd rowB
4th rowB
5th rowC
ValueCountFrequency (%)
B5268
48.6%
A2714
25.0%
C777
 
7.2%
E671
 
6.2%
?247
 
2.3%
F180
 
1.7%
O161
 
1.5%
G137
 
1.3%
L130
 
1.2%
I109
 
1.0%
Other values (15)442
 
4.1%
2021-03-23T08:52:10.473709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b5268
48.6%
a2714
25.0%
c777
 
7.2%
e671
 
6.2%
247
 
2.3%
f180
 
1.7%
o161
 
1.5%
g137
 
1.3%
l130
 
1.2%
i109
 
1.0%
Other values (15)442
 
4.1%

Most occurring characters

ValueCountFrequency (%)
B5268
48.6%
A2714
25.0%
C777
 
7.2%
E671
 
6.2%
?247
 
2.3%
F180
 
1.7%
O161
 
1.5%
G137
 
1.3%
L130
 
1.2%
I109
 
1.0%
Other values (19)450
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter10587
97.6%
Other Punctuation247
 
2.3%
Lowercase Letter10
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
B5268
49.8%
A2714
25.6%
C777
 
7.3%
E671
 
6.3%
F180
 
1.7%
O161
 
1.5%
G137
 
1.3%
L130
 
1.2%
I109
 
1.0%
M79
 
0.7%
Other values (13)361
 
3.4%
ValueCountFrequency (%)
o2
20.0%
t2
20.0%
h2
20.0%
e2
20.0%
r2
20.0%
ValueCountFrequency (%)
?247
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10597
97.7%
Common247
 
2.3%

Most frequent character per script

ValueCountFrequency (%)
B5268
49.7%
A2714
25.6%
C777
 
7.3%
E671
 
6.3%
F180
 
1.7%
O161
 
1.5%
G137
 
1.3%
L130
 
1.2%
I109
 
1.0%
M79
 
0.7%
Other values (18)371
 
3.5%
ValueCountFrequency (%)
?247
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10844
100.0%

Most frequent character per block

ValueCountFrequency (%)
B5268
48.6%
A2714
25.0%
C777
 
7.2%
E671
 
6.2%
?247
 
2.3%
F180
 
1.7%
O161
 
1.5%
G137
 
1.3%
L130
 
1.2%
I109
 
1.0%
Other values (19)450
 
4.1%

DiagnosticBlood
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
other
10060 
True
 
669
False
 
107

Length

Max length5
Median length5
Mean length4.938261351
Min length4

Characters and Unicode

Total characters53511
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other10060
92.8%
True669
 
6.2%
False107
 
1.0%
2021-03-23T08:52:10.648128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:52:10.703798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other10060
92.8%
true669
 
6.2%
false107
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e10836
20.3%
r10729
20.1%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T669
 
1.3%
u669
 
1.3%
F107
 
0.2%
a107
 
0.2%
l107
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter52735
98.5%
Uppercase Letter776
 
1.5%

Most frequent character per category

ValueCountFrequency (%)
e10836
20.5%
r10729
20.3%
o10060
19.1%
t10060
19.1%
h10060
19.1%
u669
 
1.3%
a107
 
0.2%
l107
 
0.2%
s107
 
0.2%
ValueCountFrequency (%)
T669
86.2%
F107
 
13.8%

Most occurring scripts

ValueCountFrequency (%)
Latin53511
100.0%

Most frequent character per script

ValueCountFrequency (%)
e10836
20.3%
r10729
20.1%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T669
 
1.3%
u669
 
1.3%
F107
 
0.2%
a107
 
0.2%
l107
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII53511
100.0%

Most frequent character per block

ValueCountFrequency (%)
e10836
20.3%
r10729
20.1%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T669
 
1.3%
u669
 
1.3%
F107
 
0.2%
a107
 
0.2%
l107
 
0.2%

DisfuncOrg
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
other
10060 
False
 
720
True
 
56

Length

Max length5
Median length5
Mean length4.994832041
Min length4

Characters and Unicode

Total characters54124
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other10060
92.8%
False720
 
6.6%
True56
 
0.5%
2021-03-23T08:52:10.854507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:52:11.222780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other10060
92.8%
false720
 
6.6%
true56
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e10836
20.0%
r10116
18.7%
o10060
18.6%
t10060
18.6%
h10060
18.6%
F720
 
1.3%
a720
 
1.3%
l720
 
1.3%
s720
 
1.3%
T56
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter53348
98.6%
Uppercase Letter776
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
e10836
20.3%
r10116
19.0%
o10060
18.9%
t10060
18.9%
h10060
18.9%
a720
 
1.3%
l720
 
1.3%
s720
 
1.3%
u56
 
0.1%
ValueCountFrequency (%)
F720
92.8%
T56
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
Latin54124
100.0%

Most frequent character per script

ValueCountFrequency (%)
e10836
20.0%
r10116
18.7%
o10060
18.6%
t10060
18.6%
h10060
18.6%
F720
 
1.3%
a720
 
1.3%
l720
 
1.3%
s720
 
1.3%
T56
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII54124
100.0%

Most frequent character per block

ValueCountFrequency (%)
e10836
20.0%
r10116
18.7%
o10060
18.6%
t10060
18.6%
h10060
18.6%
F720
 
1.3%
a720
 
1.3%
l720
 
1.3%
s720
 
1.3%
T56
 
0.1%

SIRSCritTachypnea
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
other
10060 
True
 
494
False
 
282

Length

Max length5
Median length5
Mean length4.954411222
Min length4

Characters and Unicode

Total characters53686
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other10060
92.8%
True494
 
4.6%
False282
 
2.6%
2021-03-23T08:52:11.374553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:52:11.430383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other10060
92.8%
true494
 
4.6%
false282
 
2.6%

Most occurring characters

ValueCountFrequency (%)
e10836
20.2%
r10554
19.7%
o10060
18.7%
t10060
18.7%
h10060
18.7%
T494
 
0.9%
u494
 
0.9%
F282
 
0.5%
a282
 
0.5%
l282
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter52910
98.6%
Uppercase Letter776
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
e10836
20.5%
r10554
19.9%
o10060
19.0%
t10060
19.0%
h10060
19.0%
u494
 
0.9%
a282
 
0.5%
l282
 
0.5%
s282
 
0.5%
ValueCountFrequency (%)
T494
63.7%
F282
36.3%

Most occurring scripts

ValueCountFrequency (%)
Latin53686
100.0%

Most frequent character per script

ValueCountFrequency (%)
e10836
20.2%
r10554
19.7%
o10060
18.7%
t10060
18.7%
h10060
18.7%
T494
 
0.9%
u494
 
0.9%
F282
 
0.5%
a282
 
0.5%
l282
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII53686
100.0%

Most frequent character per block

ValueCountFrequency (%)
e10836
20.2%
r10554
19.7%
o10060
18.7%
t10060
18.7%
h10060
18.7%
T494
 
0.9%
u494
 
0.9%
F282
 
0.5%
a282
 
0.5%
l282
 
0.5%

Hypotensie
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
other
10060 
False
 
725
True
 
51

Length

Max length5
Median length5
Mean length4.995293466
Min length4

Characters and Unicode

Total characters54129
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other10060
92.8%
False725
 
6.7%
True51
 
0.5%
2021-03-23T08:52:11.582333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:52:11.643305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other10060
92.8%
false725
 
6.7%
true51
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e10836
20.0%
r10111
18.7%
o10060
18.6%
t10060
18.6%
h10060
18.6%
F725
 
1.3%
a725
 
1.3%
l725
 
1.3%
s725
 
1.3%
T51
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter53353
98.6%
Uppercase Letter776
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
e10836
20.3%
r10111
19.0%
o10060
18.9%
t10060
18.9%
h10060
18.9%
a725
 
1.4%
l725
 
1.4%
s725
 
1.4%
u51
 
0.1%
ValueCountFrequency (%)
F725
93.4%
T51
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Latin54129
100.0%

Most frequent character per script

ValueCountFrequency (%)
e10836
20.0%
r10111
18.7%
o10060
18.6%
t10060
18.6%
h10060
18.6%
F725
 
1.3%
a725
 
1.3%
l725
 
1.3%
s725
 
1.3%
T51
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII54129
100.0%

Most frequent character per block

ValueCountFrequency (%)
e10836
20.0%
r10111
18.7%
o10060
18.6%
t10060
18.6%
h10060
18.6%
F725
 
1.3%
a725
 
1.3%
l725
 
1.3%
s725
 
1.3%
T51
 
0.1%

SIRSCritHeartRate
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
other
10060 
True
 
645
False
 
131

Length

Max length5
Median length5
Mean length4.94047619
Min length4

Characters and Unicode

Total characters53535
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other10060
92.8%
True645
 
6.0%
False131
 
1.2%
2021-03-23T08:52:11.796476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:52:11.853088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other10060
92.8%
true645
 
6.0%
false131
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e10836
20.2%
r10705
20.0%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T645
 
1.2%
u645
 
1.2%
F131
 
0.2%
a131
 
0.2%
l131
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter52759
98.6%
Uppercase Letter776
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
e10836
20.5%
r10705
20.3%
o10060
19.1%
t10060
19.1%
h10060
19.1%
u645
 
1.2%
a131
 
0.2%
l131
 
0.2%
s131
 
0.2%
ValueCountFrequency (%)
T645
83.1%
F131
 
16.9%

Most occurring scripts

ValueCountFrequency (%)
Latin53535
100.0%

Most frequent character per script

ValueCountFrequency (%)
e10836
20.2%
r10705
20.0%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T645
 
1.2%
u645
 
1.2%
F131
 
0.2%
a131
 
0.2%
l131
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII53535
100.0%

Most frequent character per block

ValueCountFrequency (%)
e10836
20.2%
r10705
20.0%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T645
 
1.2%
u645
 
1.2%
F131
 
0.2%
a131
 
0.2%
l131
 
0.2%

Infusion
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
other
10060 
True
 
656
False
 
120

Length

Max length5
Median length5
Mean length4.939461056
Min length4

Characters and Unicode

Total characters53524
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other10060
92.8%
True656
 
6.1%
False120
 
1.1%
2021-03-23T08:52:12.006742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:52:12.063537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other10060
92.8%
true656
 
6.1%
false120
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e10836
20.2%
r10716
20.0%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T656
 
1.2%
u656
 
1.2%
F120
 
0.2%
a120
 
0.2%
l120
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter52748
98.6%
Uppercase Letter776
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
e10836
20.5%
r10716
20.3%
o10060
19.1%
t10060
19.1%
h10060
19.1%
u656
 
1.2%
a120
 
0.2%
l120
 
0.2%
s120
 
0.2%
ValueCountFrequency (%)
T656
84.5%
F120
 
15.5%

Most occurring scripts

ValueCountFrequency (%)
Latin53524
100.0%

Most frequent character per script

ValueCountFrequency (%)
e10836
20.2%
r10716
20.0%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T656
 
1.2%
u656
 
1.2%
F120
 
0.2%
a120
 
0.2%
l120
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII53524
100.0%

Most frequent character per block

ValueCountFrequency (%)
e10836
20.2%
r10716
20.0%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T656
 
1.2%
u656
 
1.2%
F120
 
0.2%
a120
 
0.2%
l120
 
0.2%

DiagnosticArtAstrup
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
other
10060 
False
 
534
True
 
242

Length

Max length5
Median length5
Mean length4.977667036
Min length4

Characters and Unicode

Total characters53938
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other10060
92.8%
False534
 
4.9%
True242
 
2.2%
2021-03-23T08:52:12.217000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:52:12.273523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other10060
92.8%
false534
 
4.9%
true242
 
2.2%

Most occurring characters

ValueCountFrequency (%)
e10836
20.1%
r10302
19.1%
o10060
18.7%
t10060
18.7%
h10060
18.7%
F534
 
1.0%
a534
 
1.0%
l534
 
1.0%
s534
 
1.0%
T242
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter53162
98.6%
Uppercase Letter776
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
e10836
20.4%
r10302
19.4%
o10060
18.9%
t10060
18.9%
h10060
18.9%
a534
 
1.0%
l534
 
1.0%
s534
 
1.0%
u242
 
0.5%
ValueCountFrequency (%)
F534
68.8%
T242
31.2%

Most occurring scripts

ValueCountFrequency (%)
Latin53938
100.0%

Most frequent character per script

ValueCountFrequency (%)
e10836
20.1%
r10302
19.1%
o10060
18.7%
t10060
18.7%
h10060
18.7%
F534
 
1.0%
a534
 
1.0%
l534
 
1.0%
s534
 
1.0%
T242
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII53938
100.0%

Most frequent character per block

ValueCountFrequency (%)
e10836
20.1%
r10302
19.1%
o10060
18.7%
t10060
18.7%
h10060
18.7%
F534
 
1.0%
a534
 
1.0%
l534
 
1.0%
s534
 
1.0%
T242
 
0.4%

concept:name
Categorical

HIGH CORRELATION

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
CRP
2250 
Leucocytes
2247 
Admission NC
1029 
ER Triage
777 
ER Registration
776 
Other values (9)
3757 

Length

Max length16
Median length10
Mean length9.55915467
Min length3

Characters and Unicode

Total characters103583
Distinct characters32
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowER Registration
2nd rowLeucocytes
3rd rowCRP
4th rowLacticAcid
5th rowER Triage
ValueCountFrequency (%)
CRP2250
20.8%
Leucocytes2247
20.7%
Admission NC1029
9.5%
ER Triage777
 
7.2%
ER Registration776
 
7.2%
ER Sepsis Triage774
 
7.1%
LacticAcid771
 
7.1%
IV Antibiotics676
 
6.2%
IV Liquid618
 
5.7%
Release A585
 
5.4%
Other values (4)333
 
3.1%
2021-03-23T08:52:12.427501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
er2574
15.0%
crp2250
13.1%
leucocytes2247
13.1%
triage1551
9.0%
iv1294
7.5%
nc1029
 
6.0%
admission1029
 
6.0%
registration776
 
4.5%
sepsis774
 
4.5%
lacticacid771
 
4.5%
Other values (8)2883
16.8%

Most occurring characters

ValueCountFrequency (%)
i10741
 
10.4%
e9855
 
9.5%
s7976
 
7.7%
c7483
 
7.2%
R6518
 
6.3%
6342
 
6.1%
t6169
 
6.0%
o4728
 
4.6%
a3769
 
3.6%
L3636
 
3.5%
Other values (22)36366
35.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter69895
67.5%
Uppercase Letter27346
 
26.4%
Space Separator6342
 
6.1%

Most frequent character per category

ValueCountFrequency (%)
i10741
15.4%
e9855
14.1%
s7976
11.4%
c7483
10.7%
t6169
8.8%
o4728
6.8%
a3769
 
5.4%
u3112
 
4.5%
n2728
 
3.9%
r2574
 
3.7%
Other values (8)10760
15.4%
ValueCountFrequency (%)
R6518
23.8%
L3636
13.3%
C3303
12.1%
A3061
11.2%
E2574
 
9.4%
P2250
 
8.2%
T1551
 
5.7%
I1294
 
4.7%
V1294
 
4.7%
N1029
 
3.8%
Other values (3)836
 
3.1%
ValueCountFrequency (%)
6342
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin97241
93.9%
Common6342
 
6.1%

Most frequent character per script

ValueCountFrequency (%)
i10741
 
11.0%
e9855
 
10.1%
s7976
 
8.2%
c7483
 
7.7%
R6518
 
6.7%
t6169
 
6.3%
o4728
 
4.9%
a3769
 
3.9%
L3636
 
3.7%
C3303
 
3.4%
Other values (21)33063
34.0%
ValueCountFrequency (%)
6342
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII103583
100.0%

Most frequent character per block

ValueCountFrequency (%)
i10741
 
10.4%
e9855
 
9.5%
s7976
 
7.7%
c7483
 
7.2%
R6518
 
6.3%
6342
 
6.1%
t6169
 
6.0%
o4728
 
4.6%
a3769
 
3.6%
L3636
 
3.5%
Other values (22)36366
35.1%

Age
Real number (ℝ≥0)

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.74963086
Minimum20
Maximum90
Zeros0
Zeros (%)0.0%
Memory size84.8 KiB
2021-03-23T08:52:12.509884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile40
Q165
median75
Q385
95-th percentile90
Maximum90
Range70
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.41054766
Coefficient of variation (CV)0.2118299086
Kurtosis0.5588003326
Mean72.74963086
Median Absolute Deviation (MAD)10
Skewness-1.017068343
Sum788315
Variance237.4849791
MonotocityNot monotonic
2021-03-23T08:52:12.590158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
901850
17.1%
851738
16.0%
801500
13.8%
751331
12.3%
701153
10.6%
65758
7.0%
60722
 
6.7%
55565
 
5.2%
50404
 
3.7%
40205
 
1.9%
Other values (5)610
 
5.6%
ValueCountFrequency (%)
2031
 
0.3%
2589
0.8%
30104
1.0%
35185
1.7%
40205
1.9%
ValueCountFrequency (%)
901850
17.1%
851738
16.0%
801500
13.8%
751331
12.3%
701153
10.6%

DiagnosticIC
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
other
10060 
True
 
686
False
 
90

Length

Max length5
Median length5
Mean length4.936692506
Min length4

Characters and Unicode

Total characters53494
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other10060
92.8%
True686
 
6.3%
False90
 
0.8%
2021-03-23T08:52:12.773219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:52:12.831531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other10060
92.8%
true686
 
6.3%
false90
 
0.8%

Most occurring characters

ValueCountFrequency (%)
e10836
20.3%
r10746
20.1%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T686
 
1.3%
u686
 
1.3%
F90
 
0.2%
a90
 
0.2%
l90
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter52718
98.5%
Uppercase Letter776
 
1.5%

Most frequent character per category

ValueCountFrequency (%)
e10836
20.6%
r10746
20.4%
o10060
19.1%
t10060
19.1%
h10060
19.1%
u686
 
1.3%
a90
 
0.2%
l90
 
0.2%
s90
 
0.2%
ValueCountFrequency (%)
T686
88.4%
F90
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
Latin53494
100.0%

Most frequent character per script

ValueCountFrequency (%)
e10836
20.3%
r10746
20.1%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T686
 
1.3%
u686
 
1.3%
F90
 
0.2%
a90
 
0.2%
l90
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII53494
100.0%

Most frequent character per block

ValueCountFrequency (%)
e10836
20.3%
r10746
20.1%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T686
 
1.3%
u686
 
1.3%
F90
 
0.2%
a90
 
0.2%
l90
 
0.2%

DiagnosticSputum
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
other
10060 
False
 
753
True
 
23

Length

Max length5
Median length5
Mean length4.997877446
Min length4

Characters and Unicode

Total characters54157
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFalse
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other10060
92.8%
False753
 
6.9%
True23
 
0.2%
2021-03-23T08:52:12.990264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:52:13.048580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other10060
92.8%
false753
 
6.9%
true23
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e10836
20.0%
r10083
18.6%
o10060
18.6%
t10060
18.6%
h10060
18.6%
F753
 
1.4%
a753
 
1.4%
l753
 
1.4%
s753
 
1.4%
T23
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter53381
98.6%
Uppercase Letter776
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
e10836
20.3%
r10083
18.9%
o10060
18.8%
t10060
18.8%
h10060
18.8%
a753
 
1.4%
l753
 
1.4%
s753
 
1.4%
u23
 
< 0.1%
ValueCountFrequency (%)
F753
97.0%
T23
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin54157
100.0%

Most frequent character per script

ValueCountFrequency (%)
e10836
20.0%
r10083
18.6%
o10060
18.6%
t10060
18.6%
h10060
18.6%
F753
 
1.4%
a753
 
1.4%
l753
 
1.4%
s753
 
1.4%
T23
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII54157
100.0%

Most frequent character per block

ValueCountFrequency (%)
e10836
20.0%
r10083
18.6%
o10060
18.6%
t10060
18.6%
h10060
18.6%
F753
 
1.4%
a753
 
1.4%
l753
 
1.4%
s753
 
1.4%
T23
 
< 0.1%

DiagnosticLiquor
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
other
10065 
False
 
771

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters54180
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFalse
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other10065
92.9%
False771
 
7.1%
2021-03-23T08:52:13.193818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:52:13.248446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other10065
92.9%
false771
 
7.1%

Most occurring characters

ValueCountFrequency (%)
e10836
20.0%
o10065
18.6%
t10065
18.6%
h10065
18.6%
r10065
18.6%
F771
 
1.4%
a771
 
1.4%
l771
 
1.4%
s771
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter53409
98.6%
Uppercase Letter771
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
e10836
20.3%
o10065
18.8%
t10065
18.8%
h10065
18.8%
r10065
18.8%
a771
 
1.4%
l771
 
1.4%
s771
 
1.4%
ValueCountFrequency (%)
F771
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin54180
100.0%

Most frequent character per script

ValueCountFrequency (%)
e10836
20.0%
o10065
18.6%
t10065
18.6%
h10065
18.6%
r10065
18.6%
F771
 
1.4%
a771
 
1.4%
l771
 
1.4%
s771
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII54180
100.0%

Most frequent character per block

ValueCountFrequency (%)
e10836
20.0%
o10065
18.6%
t10065
18.6%
h10065
18.6%
r10065
18.6%
F771
 
1.4%
a771
 
1.4%
l771
 
1.4%
s771
 
1.4%

DiagnosticOther
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
other
10064 
False
 
772

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters54180
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFalse
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other10064
92.9%
False772
 
7.1%
2021-03-23T08:52:13.386111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:52:13.440650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other10064
92.9%
false772
 
7.1%

Most occurring characters

ValueCountFrequency (%)
e10836
20.0%
o10064
18.6%
t10064
18.6%
h10064
18.6%
r10064
18.6%
F772
 
1.4%
a772
 
1.4%
l772
 
1.4%
s772
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter53408
98.6%
Uppercase Letter772
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
e10836
20.3%
o10064
18.8%
t10064
18.8%
h10064
18.8%
r10064
18.8%
a772
 
1.4%
l772
 
1.4%
s772
 
1.4%
ValueCountFrequency (%)
F772
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin54180
100.0%

Most frequent character per script

ValueCountFrequency (%)
e10836
20.0%
o10064
18.6%
t10064
18.6%
h10064
18.6%
r10064
18.6%
F772
 
1.4%
a772
 
1.4%
l772
 
1.4%
s772
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII54180
100.0%

Most frequent character per block

ValueCountFrequency (%)
e10836
20.0%
o10064
18.6%
t10064
18.6%
h10064
18.6%
r10064
18.6%
F772
 
1.4%
a772
 
1.4%
l772
 
1.4%
s772
 
1.4%

SIRSCriteria2OrMore
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
other
10060 
True
 
690
False
 
86

Length

Max length5
Median length5
Mean length4.936323367
Min length4

Characters and Unicode

Total characters53490
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other10060
92.8%
True690
 
6.4%
False86
 
0.8%
2021-03-23T08:52:13.591367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:52:13.648086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other10060
92.8%
true690
 
6.4%
false86
 
0.8%

Most occurring characters

ValueCountFrequency (%)
e10836
20.3%
r10750
20.1%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T690
 
1.3%
u690
 
1.3%
F86
 
0.2%
a86
 
0.2%
l86
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter52714
98.5%
Uppercase Letter776
 
1.5%

Most frequent character per category

ValueCountFrequency (%)
e10836
20.6%
r10750
20.4%
o10060
19.1%
t10060
19.1%
h10060
19.1%
u690
 
1.3%
a86
 
0.2%
l86
 
0.2%
s86
 
0.2%
ValueCountFrequency (%)
T690
88.9%
F86
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin53490
100.0%

Most frequent character per script

ValueCountFrequency (%)
e10836
20.3%
r10750
20.1%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T690
 
1.3%
u690
 
1.3%
F86
 
0.2%
a86
 
0.2%
l86
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII53490
100.0%

Most frequent character per block

ValueCountFrequency (%)
e10836
20.3%
r10750
20.1%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T690
 
1.3%
u690
 
1.3%
F86
 
0.2%
a86
 
0.2%
l86
 
0.2%

DiagnosticXthorax
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
other
10060 
True
 
637
False
 
139

Length

Max length5
Median length5
Mean length4.94121447
Min length4

Characters and Unicode

Total characters53543
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other10060
92.8%
True637
 
5.9%
False139
 
1.3%
2021-03-23T08:52:13.800736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:52:13.857064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other10060
92.8%
true637
 
5.9%
false139
 
1.3%

Most occurring characters

ValueCountFrequency (%)
e10836
20.2%
r10697
20.0%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T637
 
1.2%
u637
 
1.2%
F139
 
0.3%
a139
 
0.3%
l139
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter52767
98.6%
Uppercase Letter776
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
e10836
20.5%
r10697
20.3%
o10060
19.1%
t10060
19.1%
h10060
19.1%
u637
 
1.2%
a139
 
0.3%
l139
 
0.3%
s139
 
0.3%
ValueCountFrequency (%)
T637
82.1%
F139
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
Latin53543
100.0%

Most frequent character per script

ValueCountFrequency (%)
e10836
20.2%
r10697
20.0%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T637
 
1.2%
u637
 
1.2%
F139
 
0.3%
a139
 
0.3%
l139
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII53543
100.0%

Most frequent character per block

ValueCountFrequency (%)
e10836
20.2%
r10697
20.0%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T637
 
1.2%
u637
 
1.2%
F139
 
0.3%
a139
 
0.3%
l139
 
0.3%

SIRSCritTemperature
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
other
10060 
True
 
645
False
 
131

Length

Max length5
Median length5
Mean length4.94047619
Min length4

Characters and Unicode

Total characters53535
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other10060
92.8%
True645
 
6.0%
False131
 
1.2%
2021-03-23T08:52:14.009982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:52:14.066684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other10060
92.8%
true645
 
6.0%
false131
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e10836
20.2%
r10705
20.0%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T645
 
1.2%
u645
 
1.2%
F131
 
0.2%
a131
 
0.2%
l131
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter52759
98.6%
Uppercase Letter776
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
e10836
20.5%
r10705
20.3%
o10060
19.1%
t10060
19.1%
h10060
19.1%
u645
 
1.2%
a131
 
0.2%
l131
 
0.2%
s131
 
0.2%
ValueCountFrequency (%)
T645
83.1%
F131
 
16.9%

Most occurring scripts

ValueCountFrequency (%)
Latin53535
100.0%

Most frequent character per script

ValueCountFrequency (%)
e10836
20.2%
r10705
20.0%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T645
 
1.2%
u645
 
1.2%
F131
 
0.2%
a131
 
0.2%
l131
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII53535
100.0%

Most frequent character per block

ValueCountFrequency (%)
e10836
20.2%
r10705
20.0%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T645
 
1.2%
u645
 
1.2%
F131
 
0.2%
a131
 
0.2%
l131
 
0.2%
Distinct7202
Distinct (%)66.5%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
Minimum2013-11-07 07:18:29+00:00
Maximum2015-06-05 10:25:11+00:00
2021-03-23T08:52:14.136117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:14.244225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

DiagnosticUrinaryCulture
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
other
10060 
True
 
388
False
 
388

Length

Max length5
Median length5
Mean length4.964193429
Min length4

Characters and Unicode

Total characters53792
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other10060
92.8%
True388
 
3.6%
False388
 
3.6%
2021-03-23T08:52:14.430693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:52:14.487160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other10060
92.8%
true388
 
3.6%
false388
 
3.6%

Most occurring characters

ValueCountFrequency (%)
e10836
20.1%
r10448
19.4%
o10060
18.7%
t10060
18.7%
h10060
18.7%
T388
 
0.7%
u388
 
0.7%
F388
 
0.7%
a388
 
0.7%
l388
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter53016
98.6%
Uppercase Letter776
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
e10836
20.4%
r10448
19.7%
o10060
19.0%
t10060
19.0%
h10060
19.0%
u388
 
0.7%
a388
 
0.7%
l388
 
0.7%
s388
 
0.7%
ValueCountFrequency (%)
T388
50.0%
F388
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin53792
100.0%

Most frequent character per script

ValueCountFrequency (%)
e10836
20.1%
r10448
19.4%
o10060
18.7%
t10060
18.7%
h10060
18.7%
T388
 
0.7%
u388
 
0.7%
F388
 
0.7%
a388
 
0.7%
l388
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII53792
100.0%

Most frequent character per block

ValueCountFrequency (%)
e10836
20.1%
r10448
19.4%
o10060
18.7%
t10060
18.7%
h10060
18.7%
T388
 
0.7%
u388
 
0.7%
F388
 
0.7%
a388
 
0.7%
l388
 
0.7%

SIRSCritLeucos
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
other
10060 
False
 
735
True
 
41

Length

Max length5
Median length5
Mean length4.996216316
Min length4

Characters and Unicode

Total characters54139
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFalse
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other10060
92.8%
False735
 
6.8%
True41
 
0.4%
2021-03-23T08:52:14.641481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:52:14.698335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other10060
92.8%
false735
 
6.8%
true41
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e10836
20.0%
r10101
18.7%
o10060
18.6%
t10060
18.6%
h10060
18.6%
F735
 
1.4%
a735
 
1.4%
l735
 
1.4%
s735
 
1.4%
T41
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter53363
98.6%
Uppercase Letter776
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
e10836
20.3%
r10101
18.9%
o10060
18.9%
t10060
18.9%
h10060
18.9%
a735
 
1.4%
l735
 
1.4%
s735
 
1.4%
u41
 
0.1%
ValueCountFrequency (%)
F735
94.7%
T41
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Latin54139
100.0%

Most frequent character per script

ValueCountFrequency (%)
e10836
20.0%
r10101
18.7%
o10060
18.6%
t10060
18.6%
h10060
18.6%
F735
 
1.4%
a735
 
1.4%
l735
 
1.4%
s735
 
1.4%
T41
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII54139
100.0%

Most frequent character per block

ValueCountFrequency (%)
e10836
20.0%
r10101
18.7%
o10060
18.6%
t10060
18.6%
h10060
18.6%
F735
 
1.4%
a735
 
1.4%
l735
 
1.4%
s735
 
1.4%
T41
 
0.1%

Oligurie
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
other
10060 
False
 
753
True
 
23

Length

Max length5
Median length5
Mean length4.997877446
Min length4

Characters and Unicode

Total characters54157
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFalse
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other10060
92.8%
False753
 
6.9%
True23
 
0.2%
2021-03-23T08:52:14.851200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:52:14.907654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other10060
92.8%
false753
 
6.9%
true23
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e10836
20.0%
r10083
18.6%
o10060
18.6%
t10060
18.6%
h10060
18.6%
F753
 
1.4%
a753
 
1.4%
l753
 
1.4%
s753
 
1.4%
T23
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter53381
98.6%
Uppercase Letter776
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
e10836
20.3%
r10083
18.9%
o10060
18.8%
t10060
18.8%
h10060
18.8%
a753
 
1.4%
l753
 
1.4%
s753
 
1.4%
u23
 
< 0.1%
ValueCountFrequency (%)
F753
97.0%
T23
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin54157
100.0%

Most frequent character per script

ValueCountFrequency (%)
e10836
20.0%
r10083
18.6%
o10060
18.6%
t10060
18.6%
h10060
18.6%
F753
 
1.4%
a753
 
1.4%
l753
 
1.4%
s753
 
1.4%
T23
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII54157
100.0%

Most frequent character per block

ValueCountFrequency (%)
e10836
20.0%
r10083
18.6%
o10060
18.6%
t10060
18.6%
h10060
18.6%
F753
 
1.4%
a753
 
1.4%
l753
 
1.4%
s753
 
1.4%
T23
 
< 0.1%

DiagnosticLacticAcid
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
other
10060 
True
 
654
False
 
122

Length

Max length5
Median length5
Mean length4.939645626
Min length4

Characters and Unicode

Total characters53526
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other10060
92.8%
True654
 
6.0%
False122
 
1.1%
2021-03-23T08:52:15.060048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:52:15.116180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other10060
92.8%
true654
 
6.0%
false122
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e10836
20.2%
r10714
20.0%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T654
 
1.2%
u654
 
1.2%
F122
 
0.2%
a122
 
0.2%
l122
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter52750
98.6%
Uppercase Letter776
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
e10836
20.5%
r10714
20.3%
o10060
19.1%
t10060
19.1%
h10060
19.1%
u654
 
1.2%
a122
 
0.2%
l122
 
0.2%
s122
 
0.2%
ValueCountFrequency (%)
T654
84.3%
F122
 
15.7%

Most occurring scripts

ValueCountFrequency (%)
Latin53526
100.0%

Most frequent character per script

ValueCountFrequency (%)
e10836
20.2%
r10714
20.0%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T654
 
1.2%
u654
 
1.2%
F122
 
0.2%
a122
 
0.2%
l122
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII53526
100.0%

Most frequent character per block

ValueCountFrequency (%)
e10836
20.2%
r10714
20.0%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T654
 
1.2%
u654
 
1.2%
F122
 
0.2%
a122
 
0.2%
l122
 
0.2%

lifecycle:transition
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
complete
10836 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters86688
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcomplete
2nd rowcomplete
3rd rowcomplete
4th rowcomplete
5th rowcomplete
ValueCountFrequency (%)
complete10836
100.0%
2021-03-23T08:52:15.257298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:52:15.308692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
complete10836
100.0%

Most occurring characters

ValueCountFrequency (%)
e21672
25.0%
c10836
12.5%
o10836
12.5%
m10836
12.5%
p10836
12.5%
l10836
12.5%
t10836
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter86688
100.0%

Most frequent character per category

ValueCountFrequency (%)
e21672
25.0%
c10836
12.5%
o10836
12.5%
m10836
12.5%
p10836
12.5%
l10836
12.5%
t10836
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin86688
100.0%

Most frequent character per script

ValueCountFrequency (%)
e21672
25.0%
c10836
12.5%
o10836
12.5%
m10836
12.5%
p10836
12.5%
l10836
12.5%
t10836
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII86688
100.0%

Most frequent character per block

ValueCountFrequency (%)
e21672
25.0%
c10836
12.5%
o10836
12.5%
m10836
12.5%
p10836
12.5%
l10836
12.5%
t10836
12.5%

Diagnose
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
other
10356 
C
 
143
B
 
81
E
 
64
H
 
49
Other values (7)
 
143

Length

Max length5
Median length5
Mean length4.822812846
Min length1

Characters and Unicode

Total characters52260
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowother
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other10356
95.6%
C143
 
1.3%
B81
 
0.7%
E64
 
0.6%
H49
 
0.5%
G42
 
0.4%
D23
 
0.2%
K22
 
0.2%
R21
 
0.2%
Q13
 
0.1%
Other values (2)22
 
0.2%
2021-03-23T08:52:15.453359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
other10356
95.6%
c143
 
1.3%
b81
 
0.7%
e64
 
0.6%
h49
 
0.5%
g42
 
0.4%
d23
 
0.2%
k22
 
0.2%
r21
 
0.2%
q13
 
0.1%
Other values (2)22
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o10356
19.8%
t10356
19.8%
h10356
19.8%
e10356
19.8%
r10356
19.8%
C143
 
0.3%
B81
 
0.2%
E64
 
0.1%
H49
 
0.1%
G42
 
0.1%
Other values (6)101
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter51780
99.1%
Uppercase Letter480
 
0.9%

Most frequent character per category

ValueCountFrequency (%)
C143
29.8%
B81
16.9%
E64
13.3%
H49
 
10.2%
G42
 
8.8%
D23
 
4.8%
K22
 
4.6%
R21
 
4.4%
Q13
 
2.7%
S12
 
2.5%
ValueCountFrequency (%)
o10356
20.0%
t10356
20.0%
h10356
20.0%
e10356
20.0%
r10356
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin52260
100.0%

Most frequent character per script

ValueCountFrequency (%)
o10356
19.8%
t10356
19.8%
h10356
19.8%
e10356
19.8%
r10356
19.8%
C143
 
0.3%
B81
 
0.2%
E64
 
0.1%
H49
 
0.1%
G42
 
0.1%
Other values (6)101
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII52260
100.0%

Most frequent character per block

ValueCountFrequency (%)
o10356
19.8%
t10356
19.8%
h10356
19.8%
e10356
19.8%
r10356
19.8%
C143
 
0.3%
B81
 
0.2%
E64
 
0.1%
H49
 
0.1%
G42
 
0.1%
Other values (6)101
 
0.2%

Hypoxie
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
other
10060 
False
 
760
True
 
16

Length

Max length5
Median length5
Mean length4.99852344
Min length4

Characters and Unicode

Total characters54164
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFalse
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other10060
92.8%
False760
 
7.0%
True16
 
0.1%
2021-03-23T08:52:15.629914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:52:15.686032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other10060
92.8%
false760
 
7.0%
true16
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e10836
20.0%
r10076
18.6%
o10060
18.6%
t10060
18.6%
h10060
18.6%
F760
 
1.4%
a760
 
1.4%
l760
 
1.4%
s760
 
1.4%
T16
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter53388
98.6%
Uppercase Letter776
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
e10836
20.3%
r10076
18.9%
o10060
18.8%
t10060
18.8%
h10060
18.8%
a760
 
1.4%
l760
 
1.4%
s760
 
1.4%
u16
 
< 0.1%
ValueCountFrequency (%)
F760
97.9%
T16
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin54164
100.0%

Most frequent character per script

ValueCountFrequency (%)
e10836
20.0%
r10076
18.6%
o10060
18.6%
t10060
18.6%
h10060
18.6%
F760
 
1.4%
a760
 
1.4%
l760
 
1.4%
s760
 
1.4%
T16
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII54164
100.0%

Most frequent character per block

ValueCountFrequency (%)
e10836
20.0%
r10076
18.6%
o10060
18.6%
t10060
18.6%
h10060
18.6%
F760
 
1.4%
a760
 
1.4%
l760
 
1.4%
s760
 
1.4%
T16
 
< 0.1%

DiagnosticUrinarySediment
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
other
10060 
True
 
423
False
 
353

Length

Max length5
Median length5
Mean length4.960963455
Min length4

Characters and Unicode

Total characters53757
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other10060
92.8%
True423
 
3.9%
False353
 
3.3%
2021-03-23T08:52:15.840282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:52:15.896869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other10060
92.8%
true423
 
3.9%
false353
 
3.3%

Most occurring characters

ValueCountFrequency (%)
e10836
20.2%
r10483
19.5%
o10060
18.7%
t10060
18.7%
h10060
18.7%
T423
 
0.8%
u423
 
0.8%
F353
 
0.7%
a353
 
0.7%
l353
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter52981
98.6%
Uppercase Letter776
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
e10836
20.5%
r10483
19.8%
o10060
19.0%
t10060
19.0%
h10060
19.0%
u423
 
0.8%
a353
 
0.7%
l353
 
0.7%
s353
 
0.7%
ValueCountFrequency (%)
T423
54.5%
F353
45.5%

Most occurring scripts

ValueCountFrequency (%)
Latin53757
100.0%

Most frequent character per script

ValueCountFrequency (%)
e10836
20.2%
r10483
19.5%
o10060
18.7%
t10060
18.7%
h10060
18.7%
T423
 
0.8%
u423
 
0.8%
F353
 
0.7%
a353
 
0.7%
l353
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII53757
100.0%

Most frequent character per block

ValueCountFrequency (%)
e10836
20.2%
r10483
19.5%
o10060
18.7%
t10060
18.7%
h10060
18.7%
T423
 
0.8%
u423
 
0.8%
F353
 
0.7%
a353
 
0.7%
l353
 
0.7%

DiagnosticECG
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
other
10060 
True
 
623
False
 
153

Length

Max length5
Median length5
Mean length4.94250646
Min length4

Characters and Unicode

Total characters53557
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other10060
92.8%
True623
 
5.7%
False153
 
1.4%
2021-03-23T08:52:16.049776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:52:16.397169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other10060
92.8%
true623
 
5.7%
false153
 
1.4%

Most occurring characters

ValueCountFrequency (%)
e10836
20.2%
r10683
19.9%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T623
 
1.2%
u623
 
1.2%
F153
 
0.3%
a153
 
0.3%
l153
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter52781
98.6%
Uppercase Letter776
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
e10836
20.5%
r10683
20.2%
o10060
19.1%
t10060
19.1%
h10060
19.1%
u623
 
1.2%
a153
 
0.3%
l153
 
0.3%
s153
 
0.3%
ValueCountFrequency (%)
T623
80.3%
F153
 
19.7%

Most occurring scripts

ValueCountFrequency (%)
Latin53557
100.0%

Most frequent character per script

ValueCountFrequency (%)
e10836
20.2%
r10683
19.9%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T623
 
1.2%
u623
 
1.2%
F153
 
0.3%
a153
 
0.3%
l153
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII53557
100.0%

Most frequent character per block

ValueCountFrequency (%)
e10836
20.2%
r10683
19.9%
o10060
18.8%
t10060
18.8%
h10060
18.8%
T623
 
1.2%
u623
 
1.2%
F153
 
0.3%
a153
 
0.3%
l153
 
0.3%

case
Categorical

HIGH CARDINALITY

Distinct776
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
GK
 
60
JS
 
39
PU
 
38
ED
 
33
RG
 
33
Other values (771)
10633 

Length

Max length7
Median length2
Mean length2.291251384
Min length1

Characters and Unicode

Total characters24828
Distinct characters31
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA
ValueCountFrequency (%)
GK60
 
0.6%
JS39
 
0.4%
PU38
 
0.4%
ED33
 
0.3%
RG33
 
0.3%
SK32
 
0.3%
LJ31
 
0.3%
UD31
 
0.3%
MW30
 
0.3%
EQ30
 
0.3%
Other values (766)10479
96.7%
2021-03-23T08:52:16.592046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gk60
 
0.6%
js39
 
0.4%
pu38
 
0.4%
rg33
 
0.3%
ed33
 
0.3%
sk32
 
0.3%
lj31
 
0.3%
ud31
 
0.3%
uda30
 
0.3%
yp30
 
0.3%
Other values (766)10479
96.7%

Most occurring characters

ValueCountFrequency (%)
A4072
 
16.4%
K1090
 
4.4%
G1054
 
4.2%
B1032
 
4.2%
J992
 
4.0%
L983
 
4.0%
H952
 
3.8%
I927
 
3.7%
D920
 
3.7%
E905
 
3.6%
Other values (21)11901
47.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter24660
99.3%
Lowercase Letter168
 
0.7%

Most frequent character per category

ValueCountFrequency (%)
A4072
 
16.5%
K1090
 
4.4%
G1054
 
4.3%
B1032
 
4.2%
J992
 
4.0%
L983
 
4.0%
H952
 
3.9%
I927
 
3.8%
D920
 
3.7%
E905
 
3.7%
Other values (16)11733
47.6%
ValueCountFrequency (%)
i48
28.6%
s48
28.6%
m24
14.3%
n24
14.3%
g24
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin24828
100.0%

Most frequent character per script

ValueCountFrequency (%)
A4072
 
16.4%
K1090
 
4.4%
G1054
 
4.2%
B1032
 
4.2%
J992
 
4.0%
L983
 
4.0%
H952
 
3.8%
I927
 
3.7%
D920
 
3.7%
E905
 
3.6%
Other values (21)11901
47.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII24828
100.0%

Most frequent character per block

ValueCountFrequency (%)
A4072
 
16.4%
K1090
 
4.4%
G1054
 
4.2%
B1032
 
4.2%
J992
 
4.0%
L983
 
4.0%
H952
 
3.8%
I927
 
3.7%
D920
 
3.7%
E905
 
3.6%
Other values (21)11901
47.9%

Leucocytes
Real number (ℝ≥0)

ZEROS

Distinct315
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.33605574
Minimum0
Maximum381.3
Zeros3069
Zeros (%)28.3%
Memory size84.8 KiB
2021-03-23T08:52:16.690617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8.45
Q313.3
95-th percentile22.8
Maximum381.3
Range381.3
Interquartile range (IQR)13.3

Descriptive statistics

Standard deviation14.76064713
Coefficient of variation (CV)1.581036739
Kurtosis232.259072
Mean9.33605574
Median Absolute Deviation (MAD)6.35
Skewness12.30990975
Sum101165.5
Variance217.8767036
MonotocityNot monotonic
2021-03-23T08:52:16.799394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03069
28.3%
11.494
 
0.9%
9.686
 
0.8%
6.782
 
0.8%
10.182
 
0.8%
8.781
 
0.7%
8.278
 
0.7%
13.177
 
0.7%
14.576
 
0.7%
10.876
 
0.7%
Other values (305)7035
64.9%
ValueCountFrequency (%)
03069
28.3%
0.214
 
0.1%
0.316
 
0.1%
0.45
 
< 0.1%
0.521
 
0.2%
ValueCountFrequency (%)
381.33
< 0.1%
297.61
 
< 0.1%
296.25
< 0.1%
234.23
< 0.1%
199.82
 
< 0.1%

CRP
Real number (ℝ≥0)

ZEROS

Distinct338
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.35308232
Minimum0
Maximum573
Zeros3408
Zeros (%)31.5%
Memory size84.8 KiB
2021-03-23T08:52:16.912623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median43
Q3123
95-th percentile266
Maximum573
Range573
Interquartile range (IQR)123

Descriptive statistics

Standard deviation89.16622407
Coefficient of variation (CV)1.183312233
Kurtosis1.869327059
Mean75.35308232
Median Absolute Deviation (MAD)43
Skewness1.417696961
Sum816526
Variance7950.615515
MonotocityNot monotonic
2021-03-23T08:52:17.019057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03408
31.5%
1791
 
0.8%
1188
 
0.8%
876
 
0.7%
975
 
0.7%
1675
 
0.7%
1471
 
0.7%
1271
 
0.7%
770
 
0.6%
2070
 
0.6%
Other values (328)6741
62.2%
ValueCountFrequency (%)
03408
31.5%
533
 
0.3%
667
 
0.6%
770
 
0.6%
876
 
0.7%
ValueCountFrequency (%)
5733
< 0.1%
5163
< 0.1%
4777
0.1%
4737
0.1%
4343
< 0.1%

LacticAcid
Real number (ℝ≥0)

ZEROS

Distinct63
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.167635659
Minimum0
Maximum9.6
Zeros4027
Zeros (%)37.2%
Memory size84.8 KiB
2021-03-23T08:52:17.133257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.1
Q31.8
95-th percentile3.2
Maximum9.6
Range9.6
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.222721356
Coefficient of variation (CV)1.047177128
Kurtosis5.674610375
Mean1.167635659
Median Absolute Deviation (MAD)1.1
Skewness1.63807239
Sum12652.5
Variance1.495047515
MonotocityNot monotonic
2021-03-23T08:52:17.245273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04027
37.2%
1.3476
 
4.4%
1.2429
 
4.0%
1.5418
 
3.9%
1.1394
 
3.6%
1.9380
 
3.5%
1.6367
 
3.4%
1.4352
 
3.2%
1335
 
3.1%
1.8322
 
3.0%
Other values (53)3336
30.8%
ValueCountFrequency (%)
04027
37.2%
0.29
 
0.1%
0.420
 
0.2%
0.516
 
0.1%
0.677
 
0.7%
ValueCountFrequency (%)
9.69
0.1%
9.54
 
< 0.1%
8.22
 
< 0.1%
8.16
0.1%
7.610
0.1%

openCases
Real number (ℝ≥0)

Distinct94
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.27196382
Minimum0
Maximum93
Zeros1
Zeros (%)< 0.1%
Memory size84.8 KiB
2021-03-23T08:52:17.355510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q150
median77
Q384
95-th percentile90
Maximum93
Range93
Interquartile range (IQR)34

Descriptive statistics

Standard deviation22.49222708
Coefficient of variation (CV)0.3393927957
Kurtosis-0.2363396109
Mean66.27196382
Median Absolute Deviation (MAD)10
Skewness-0.9585487144
Sum718123
Variance505.900279
MonotocityNot monotonic
2021-03-23T08:52:17.461437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83503
 
4.6%
84500
 
4.6%
87488
 
4.5%
79412
 
3.8%
82410
 
3.8%
78398
 
3.7%
77352
 
3.2%
85345
 
3.2%
80326
 
3.0%
86316
 
2.9%
Other values (84)6786
62.6%
ValueCountFrequency (%)
01
 
< 0.1%
111
0.1%
211
0.1%
312
0.1%
413
0.1%
ValueCountFrequency (%)
9332
 
0.3%
92144
1.3%
91116
1.1%
90252
2.3%
89276
2.5%

weekday
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.962255445
Minimum0
Maximum6
Zeros1639
Zeros (%)15.1%
Memory size84.8 KiB
2021-03-23T08:52:17.549978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.012732028
Coefficient of variation (CV)0.6794593056
Kurtosis-1.250354073
Mean2.962255445
Median Absolute Deviation (MAD)2
Skewness0.03554651059
Sum32099
Variance4.051090215
MonotocityNot monotonic
2021-03-23T08:52:17.622768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
21663
15.3%
01639
15.1%
31578
14.6%
61573
14.5%
51506
13.9%
11479
13.6%
41398
12.9%
ValueCountFrequency (%)
01639
15.1%
11479
13.6%
21663
15.3%
31578
14.6%
41398
12.9%
ValueCountFrequency (%)
61573
14.5%
51506
13.9%
41398
12.9%
31578
14.6%
21663
15.3%

month
Real number (ℝ≥0)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.489940938
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size84.8 KiB
2021-03-23T08:52:17.704753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.587754176
Coefficient of variation (CV)0.5528176928
Kurtosis-1.331583011
Mean6.489940938
Median Absolute Deviation (MAD)3
Skewness0.01435486084
Sum70325
Variance12.87198002
MonotocityNot monotonic
2021-03-23T08:52:17.778071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
111126
10.4%
1992
9.2%
3990
9.1%
10983
9.1%
5982
9.1%
2971
9.0%
12940
8.7%
8900
8.3%
4846
7.8%
6759
7.0%
Other values (2)1347
12.4%
ValueCountFrequency (%)
1992
9.2%
2971
9.0%
3990
9.1%
4846
7.8%
5982
9.1%
ValueCountFrequency (%)
12940
8.7%
111126
10.4%
10983
9.1%
9724
6.7%
8900
8.3%

hour
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.45099668
Minimum0
Maximum23
Zeros175
Zeros (%)1.6%
Memory size84.8 KiB
2021-03-23T08:52:17.856914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q17
median11
Q316
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.582530759
Coefficient of variation (CV)0.4875148353
Kurtosis-0.8356254427
Mean11.45099668
Median Absolute Deviation (MAD)4
Skewness0.2699412929
Sum124083
Variance31.16464968
MonotocityNot monotonic
2021-03-23T08:52:17.941625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
61447
 
13.4%
71400
 
12.9%
9632
 
5.8%
12566
 
5.2%
13558
 
5.1%
10558
 
5.1%
16516
 
4.8%
14508
 
4.7%
11500
 
4.6%
8500
 
4.6%
Other values (14)3651
33.7%
ValueCountFrequency (%)
0175
1.6%
1113
1.0%
2134
1.2%
3112
1.0%
4131
1.2%
ValueCountFrequency (%)
23191
1.8%
22239
2.2%
21363
3.3%
20374
3.5%
19436
4.0%

day
Real number (ℝ≥0)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.60289775
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size84.8 KiB
2021-03-23T08:52:18.030005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q324
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.913967455
Coefficient of variation (CV)0.571302049
Kurtosis-1.222107487
Mean15.60289775
Median Absolute Deviation (MAD)8
Skewness0.04330135154
Sum169073
Variance79.45881578
MonotocityNot monotonic
2021-03-23T08:52:18.116114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
27452
 
4.2%
2450
 
4.2%
7436
 
4.0%
11429
 
4.0%
25422
 
3.9%
17421
 
3.9%
12395
 
3.6%
5388
 
3.6%
16375
 
3.5%
9373
 
3.4%
Other values (21)6695
61.8%
ValueCountFrequency (%)
1368
3.4%
2450
4.2%
3289
2.7%
4322
3.0%
5388
3.6%
ValueCountFrequency (%)
31233
2.2%
30311
2.9%
29339
3.1%
28317
2.9%
27452
4.2%

timesincemidnight
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1313
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean707.6491325
Minimum0
Maximum1439
Zeros4
Zeros (%)< 0.1%
Memory size84.8 KiB
2021-03-23T08:52:18.217765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile245
Q1420
median662
Q3978
95-th percentile1302
Maximum1439
Range1439
Interquartile range (IQR)558

Descriptive statistics

Standard deviation341.4318319
Coefficient of variation (CV)0.4824874591
Kurtosis-0.8990713123
Mean707.6491325
Median Absolute Deviation (MAD)253
Skewness0.2751209852
Sum7668086
Variance116575.6958
MonotocityNot monotonic
2021-03-23T08:52:18.322876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3601293
 
11.9%
4201147
 
10.6%
540173
 
1.6%
600122
 
1.1%
720121
 
1.1%
780114
 
1.1%
48085
 
0.8%
30063
 
0.6%
66050
 
0.5%
84045
 
0.4%
Other values (1303)7623
70.3%
ValueCountFrequency (%)
04
< 0.1%
14
< 0.1%
33
< 0.1%
55
< 0.1%
66
0.1%
ValueCountFrequency (%)
14391
 
< 0.1%
14385
< 0.1%
14373
< 0.1%
14362
 
< 0.1%
14352
 
< 0.1%

timesincelast
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct3728
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192778.2779
Minimum0
Maximum36051318
Zeros3596
Zeros (%)33.2%
Memory size84.8 KiB
2021-03-23T08:52:18.436006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median265
Q320498.5
95-th percentile270000
Maximum36051318
Range36051318
Interquartile range (IQR)20498.5

Descriptive statistics

Standard deviation1552811.659
Coefficient of variation (CV)8.054909902
Kurtosis217.67254
Mean192778.2779
Median Absolute Deviation (MAD)265
Skewness13.59408104
Sum2088945419
Variance2.411224049 × 1012
MonotocityNot monotonic
2021-03-23T08:52:18.542218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03596
33.2%
172800254
 
2.3%
86400235
 
2.2%
25920089
 
0.8%
1557
 
0.5%
156
 
0.5%
450
 
0.5%
1647
 
0.4%
34560047
 
0.4%
545
 
0.4%
Other values (3718)6360
58.7%
ValueCountFrequency (%)
03596
33.2%
156
 
0.5%
27
 
0.1%
326
 
0.2%
450
 
0.5%
ValueCountFrequency (%)
360513181
< 0.1%
343013341
< 0.1%
340583661
< 0.1%
321416951
< 0.1%
310631161
< 0.1%

timesincestart
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6135
Distinct (%)56.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean324860.8772
Minimum0
Maximum36488789
Zeros794
Zeros (%)7.3%
Memory size84.8 KiB
2021-03-23T08:52:18.656711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11043
median8673.5
Q3211909
95-th percentile1021552
Maximum36488789
Range36488789
Interquartile range (IQR)210866

Descriptive statistics

Standard deviation1624798.794
Coefficient of variation (CV)5.001521907
Kurtosis191.8776469
Mean324860.8772
Median Absolute Deviation (MAD)8547.5
Skewness12.51537886
Sum3520192465
Variance2.63997112 × 1012
MonotocityNot monotonic
2021-03-23T08:52:18.762886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0794
 
7.3%
113712
 
0.1%
172110
 
0.1%
75910
 
0.1%
61910
 
0.1%
156910
 
0.1%
7309
 
0.1%
13089
 
0.1%
15529
 
0.1%
14579
 
0.1%
Other values (6125)9954
91.9%
ValueCountFrequency (%)
0794
7.3%
31
 
< 0.1%
141
 
< 0.1%
161
 
< 0.1%
271
 
< 0.1%
ValueCountFrequency (%)
364887891
< 0.1%
344670031
< 0.1%
342980701
< 0.1%
334342001
< 0.1%
315311161
< 0.1%

remainingtime
Real number (ℝ≥0)

ZEROS

Distinct6830
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3001923.4
Minimum0
Maximum36488789
Zeros677
Zeros (%)6.2%
Memory size84.8 KiB
2021-03-23T08:52:18.877587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1323340
median700200
Q32558997.25
95-th percentile15572776.25
Maximum36488789
Range36488789
Interquartile range (IQR)2235657.25

Descriptive statistics

Standard deviation5603984.927
Coefficient of variation (CV)1.866798109
Kurtosis10.50167917
Mean3001923.4
Median Absolute Deviation (MAD)521100
Skewness3.069336766
Sum3.252884197 × 1010
Variance3.140464706 × 1013
MonotocityNot monotonic
2021-03-23T08:52:18.983776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0677
 
6.2%
9360032
 
0.3%
18000028
 
0.3%
28080023
 
0.2%
9720022
 
0.2%
26640018
 
0.2%
2160018
 
0.2%
52920017
 
0.2%
10800016
 
0.1%
27000015
 
0.1%
Other values (6820)9970
92.0%
ValueCountFrequency (%)
0677
6.2%
8101
 
< 0.1%
8231
 
< 0.1%
9601
 
< 0.1%
11971
 
< 0.1%
ValueCountFrequency (%)
364887891
 
< 0.1%
364885731
 
< 0.1%
364885541
 
< 0.1%
364879383
< 0.1%
364820271
 
< 0.1%

OrderOfEvent
Real number (ℝ≥0)

Distinct60
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.393964563
Minimum1
Maximum60
Zeros0
Zeros (%)0.0%
Memory size84.8 KiB
2021-03-23T08:52:19.096241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q312
95-th percentile18
Maximum60
Range59
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.845647001
Coefficient of variation (CV)0.6964107315
Kurtosis6.507280585
Mean8.393964563
Median Absolute Deviation (MAD)4
Skewness1.634146384
Sum90957
Variance34.17158886
MonotocityNot monotonic
2021-03-23T08:52:19.202355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1776
 
7.2%
2776
 
7.2%
3776
 
7.2%
4776
 
7.2%
5775
 
7.2%
6768
 
7.1%
7761
 
7.0%
8741
 
6.8%
9680
 
6.3%
10648
 
6.0%
Other values (50)3359
31.0%
ValueCountFrequency (%)
1776
7.2%
2776
7.2%
3776
7.2%
4776
7.2%
5775
7.2%
ValueCountFrequency (%)
601
< 0.1%
591
< 0.1%
581
< 0.1%
571
< 0.1%
561
< 0.1%

label
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.8 KiB
regular
9853 
deviant
 
983

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters75852
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowregular
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular
ValueCountFrequency (%)
regular9853
90.9%
deviant983
 
9.1%
2021-03-23T08:52:19.378441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:52:19.430707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
regular9853
90.9%
deviant983
 
9.1%

Most occurring characters

ValueCountFrequency (%)
r19706
26.0%
e10836
14.3%
a10836
14.3%
g9853
13.0%
u9853
13.0%
l9853
13.0%
d983
 
1.3%
v983
 
1.3%
i983
 
1.3%
n983
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter75852
100.0%

Most frequent character per category

ValueCountFrequency (%)
r19706
26.0%
e10836
14.3%
a10836
14.3%
g9853
13.0%
u9853
13.0%
l9853
13.0%
d983
 
1.3%
v983
 
1.3%
i983
 
1.3%
n983
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Latin75852
100.0%

Most frequent character per script

ValueCountFrequency (%)
r19706
26.0%
e10836
14.3%
a10836
14.3%
g9853
13.0%
u9853
13.0%
l9853
13.0%
d983
 
1.3%
v983
 
1.3%
i983
 
1.3%
n983
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII75852
100.0%

Most frequent character per block

ValueCountFrequency (%)
r19706
26.0%
e10836
14.3%
a10836
14.3%
g9853
13.0%
u9853
13.0%
l9853
13.0%
d983
 
1.3%
v983
 
1.3%
i983
 
1.3%
n983
 
1.3%

Interactions

2021-03-23T08:51:50.424633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:50.534250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:50.636356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:50.736031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:50.833564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:50.934528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:51.033412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:51.130119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:51.226798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:51.327196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:51.428410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:51.529932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:51.631382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:51.729651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:51.831666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:51.931437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:52.028883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:52.124745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:52.224207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:52.321572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:52.418191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:52.519745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:52.626099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:52.730640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:52.835421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:52.939577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:53.039989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:53.135225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:53.234652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:53.325911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:53.415425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:53.508288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:53.599494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:53.688507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:53.781302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:53.878451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:53.976251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:54.074675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:54.172552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:54.266454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:54.358939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:54.456847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:54.547373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:54.633995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:54.724308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:54.813127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:54.899263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:54.989544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:55.084010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:55.179225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:55.274710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:55.369864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:55.460958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:55.550921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:55.643996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:55.731704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:55.817132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:55.904146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:55.989254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:56.072134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:56.479128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:56.571031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:56.663089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:56.755313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:56.847855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:56.936188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:57.032203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:57.130973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:57.224797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:57.315583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:57.404961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:57.496202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:57.585420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:57.679199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:57.776048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:57.877816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:57.975852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:58.075288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:58.171753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:58.263978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:58.359477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:58.449748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:58.537822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:58.624001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:58.713656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:58.798958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:58.888508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:58.981880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:59.076663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:59.171262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:59.266193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:59.357422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:59.446595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:59.538323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:59.625126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:59.709432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:59.792561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:59.878892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:51:59.962964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:00.049266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:00.139444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:00.230972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:00.322595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:00.414156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:00.501307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:00.596645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:00.694482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:00.787117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:00.878595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:00.967635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:01.059855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:01.150122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:01.238257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:01.334692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:01.431959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:01.529453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:01.627418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:01.720465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:01.819638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:01.922116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:02.019553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:02.114705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:02.208356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:02.305265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:02.401259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:02.494624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:02.591739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:02.693676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:02.795748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:02.901232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:02.999374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:03.406067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:03.516461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:03.620879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:03.722701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:03.823190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:03.927916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:04.030201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:04.129512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:04.233394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:04.341367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:04.450252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:04.555228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:04.655100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:04.756581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:04.861182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:04.960414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:05.057453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:05.152729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:05.251407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:05.348146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:05.443189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:05.542035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:05.645891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:05.749511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:05.853649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:05.953221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:06.054382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:06.158612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:06.257528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:06.354194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:06.449492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:06.547859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:06.644471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:06.738473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:06.836981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:06.939662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:07.044588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:07.148036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:07.247471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:07.343492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:07.442627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:07.536163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:07.627728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:07.717854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:07.811236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:07.902589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:07.991594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:08.085323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:08.182528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:08.280970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:52:08.379292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-03-23T08:52:19.495449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-23T08:52:19.664035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-23T08:52:19.830894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-23T08:52:20.040022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-23T08:52:20.367967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-23T08:52:08.664522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-23T08:52:09.835624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

InfectionSuspectedorg:groupDiagnosticBloodDisfuncOrgSIRSCritTachypneaHypotensieSIRSCritHeartRateInfusionDiagnosticArtAstrupconcept:nameAgeDiagnosticICDiagnosticSputumDiagnosticLiquorDiagnosticOtherSIRSCriteria2OrMoreDiagnosticXthoraxSIRSCritTemperaturetime:timestampDiagnosticUrinaryCultureSIRSCritLeucosOligurieDiagnosticLacticAcidlifecycle:transitionDiagnoseHypoxieDiagnosticUrinarySedimentDiagnosticECGcaseLeucocytesCRPLacticAcidopenCasesweekdaymonthhourdaytimesincemidnighttimesincelasttimesincestartremainingtimeOrderOfEventlabel
0TrueATrueTrueTrueTrueTrueTrueTrueER Registration85.0TrueFalseFalseFalseTrueTrueTrue2014-10-22 09:15:41+00:00TrueFalseFalseTruecompleteotherFalseTrueTrueA0.00.00.081.0210.09.022.0555.00.00.0968359.01.0regular
1otherBotherotherotherotherotherotherotherLeucocytes85.0otherotherotherotherotherotherother2014-10-22 09:27:00+00:00otherotherotherothercompleteotherotherotherotherA9.60.00.081.0210.09.022.0567.00.0679.0967680.02.0regular
2otherBotherotherotherotherotherotherotherCRP85.0otherotherotherotherotherotherother2014-10-22 09:27:00+00:00otherotherotherothercompleteotherotherotherotherA9.621.00.081.0210.09.022.0567.00.0679.0967680.03.0regular
3otherBotherotherotherotherotherotherotherLacticAcid85.0otherotherotherotherotherotherother2014-10-22 09:27:00+00:00otherotherotherothercompleteotherotherotherotherA9.621.02.281.0210.09.022.0567.0679.0679.0967680.04.0regular
4otherCotherotherotherotherotherotherotherER Triage85.0otherotherotherotherotherotherother2014-10-22 09:33:37+00:00otherotherotherothercompleteotherotherotherotherA9.621.02.281.0210.09.022.0573.0397.01076.0967283.05.0regular
5otherAotherotherotherotherotherotherotherER Sepsis Triage85.0otherotherotherotherotherotherother2014-10-22 09:34:00+00:00otherotherotherothercompleteotherotherotherotherA9.621.02.281.0210.09.022.0574.023.01099.0967260.06.0regular
6otherAotherotherotherotherotherotherotherIV Liquid85.0otherotherotherotherotherotherother2014-10-22 12:03:47+00:00otherotherotherothercompleteotherotherotherotherA9.621.02.281.0210.012.022.0723.00.010086.0958273.07.0regular
7otherAotherotherotherotherotherotherotherIV Antibiotics85.0otherotherotherotherotherotherother2014-10-22 12:03:47+00:00otherotherotherothercompleteotherotherotherotherA9.621.02.281.0210.012.022.0723.08987.010086.0958273.08.0regular
8otherDotherotherotherotherotherotherotherAdmission NC85.0otherotherotherotherotherotherother2014-10-22 12:13:19+00:00otherotherotherothercompleteotherotherotherotherA9.621.02.281.0210.012.022.0733.0572.010658.0957701.09.0regular
9otherBotherotherotherotherotherotherotherCRP85.0otherotherotherotherotherotherother2014-10-24 07:00:00+00:00otherotherotherothercompleteotherotherotherotherA9.6109.02.279.0410.07.024.0420.00.0164659.0803700.010.0regular

Last rows

InfectionSuspectedorg:groupDiagnosticBloodDisfuncOrgSIRSCritTachypneaHypotensieSIRSCritHeartRateInfusionDiagnosticArtAstrupconcept:nameAgeDiagnosticICDiagnosticSputumDiagnosticLiquorDiagnosticOtherSIRSCriteria2OrMoreDiagnosticXthoraxSIRSCritTemperaturetime:timestampDiagnosticUrinaryCultureSIRSCritLeucosOligurieDiagnosticLacticAcidlifecycle:transitionDiagnoseHypoxieDiagnosticUrinarySedimentDiagnosticECGcaseLeucocytesCRPLacticAcidopenCasesweekdaymonthhourdaytimesincemidnighttimesincelasttimesincestartremainingtimeOrderOfEventlabel
10826otherBotherotherotherotherotherotherotherLeucocytes90.0otherotherotherotherotherotherother2014-11-15 15:08:00+00:00otherotherotherothercompleteotherotherotherothermissing19.0207.01.075.0511.015.015.0908.0205680.0483761.0598920.015.0regular
10827otherBotherotherotherotherotherotherotherLeucocytes90.0otherotherotherotherotherotherother2014-11-15 18:42:00+00:00otherotherotherothercompleteotherotherotherothermissing17.1207.01.076.0511.018.015.01122.012840.0496601.0586080.016.0regular
10828otherBotherotherotherotherotherotherotherLeucocytes90.0otherotherotherotherotherotherother2014-11-15 22:46:00+00:00otherotherotherothercompleteotherotherotherothermissing17.7207.01.076.0511.022.015.01366.014640.0511241.0571440.017.0regular
10829otherBotherotherotherotherotherotherotherLeucocytes90.0otherotherotherotherotherotherother2014-11-16 06:00:00+00:00otherotherotherothercompleteotherotherotherothermissing17.2207.01.077.0611.06.016.0360.00.0537281.0545400.018.0regular
10830otherBotherotherotherotherotherotherotherCRP90.0otherotherotherotherotherotherother2014-11-16 06:00:00+00:00otherotherotherothercompleteotherotherotherothermissing17.278.01.077.0611.06.016.0360.026040.0537281.0545400.019.0regular
10831otherBotherotherotherotherotherotherotherLacticAcid90.0otherotherotherotherotherotherother2014-11-16 12:05:00+00:00otherotherotherothercompleteotherotherotherothermissing17.278.01.775.0611.012.016.0725.021900.0559181.0523500.020.0regular
10832otherBotherotherotherotherotherotherotherLeucocytes90.0otherotherotherotherotherotherother2014-11-17 06:00:00+00:00otherotherotherothercompleteotherotherotherothermissing17.478.01.777.0011.06.017.0360.00.0623681.0459000.021.0regular
10833otherBotherotherotherotherotherotherotherCRP90.0otherotherotherotherotherotherother2014-11-17 06:00:00+00:00otherotherotherothercompleteotherotherotherothermissing17.464.01.777.0011.06.017.0360.064500.0623681.0459000.022.0regular
10834otherBotherotherotherotherotherotherotherLeucocytes90.0otherotherotherotherotherotherother2014-11-18 06:00:00+00:00otherotherotherothercompleteotherotherotherothermissing16.864.01.778.0111.06.018.0360.086400.0710081.0372600.023.0regular
10835otherEotherotherotherotherotherotherotherRelease C90.0otherotherotherotherotherotherother2014-11-22 13:30:00+00:00otherotherotherothercompleteotherotherotherothermissing16.864.01.770.0511.013.022.0810.0372600.01082681.00.024.0regular